Computational methods and analysis of tumour genome sequence data
Project type
PhD
| Supervisor(s) | Division | |
| (Primary) | Bioinformatics | .(JavaScript must be enabled to view this email address) |
| (Co-supervisor) | Bioinformatics | .(JavaScript must be enabled to view this email address) |
Details of project
We are involved in several tumour genome, exome and transcriptome sequencing projects (e.g. sarcoma, melanoma, breast cancer, Tasmanian devil facial tumour disease). These projects are generating vast amounts of next generation sequencing data and there are many computational and statistical challenges that arise.
A central focus of this PhD project will be the development of new tools to analyse and explore tumour genome and transcriptome data. We are particularly interested in developing new methods for predicting structural variation in the data and discovering the transcriptional consequences of changes in the tumour using RNA-seq data, but issues like GC correction, normalisation and data integration also arise. Ultimately the challenge is to derive biological and clinical meaning from our analyses.
Project references
- Stephens et al. Massive genomic rearrangement acquired in a single catastrophic event during cancer development. Cell. 2011 144(1):27-40
- Medvedev et al. Computational methods for discovering structural variation with next-generation sequencing. Nat Methods. 2009 6(11):S13-20
- Campbell et al. Subclonal phylogenetic structures in cancer revealed by ultra-deep sequencing. Proc Natl Acad Sci USA. 2008 105(35):13081-6
- Gisselsson et al. Chromosomal breakage-fusion-bridge events cause genetic intratumor heterogeneity. Proc Natl Acad Sci USA. 2000 97(10):5357-62
- Sandberg. Updates on the cytogenetics and molecular genetics of bone and soft tissue tumors: liposarcoma. Cancer Genet Cytogenet. 2004 155(1):1-24
Research interests
Our research interests are in the area of biological sequence analysis. This is an area of bioinformatics where ideas from mathematics, statistics and computer science are applies to the analysis of genome, transcript or protein sequence data. We frequently work in close partnership with the biologists and clinicians who generate the data.
Much of our efforts are now focused on the analysis of next generation sequence data and the development of new or boutique methods to improve analyses. A secondary focus of our group is computational comparative genomics. Here we use of sequence data from multiple species to learn about the evolution of genes related to disease. This has also been the basis of approaches to sensitive genome searching that we have developed and applied to study genes in malaria and to discover novel genes in mammalian genomes.
Research theme
Cancer
Scientific discipline
- Bioinformatics
- Computer Science
- Genomics
- Mathematics
- Statistics
Keywords
Next generation sequencing; tumour genomes; computational biology